Fast Fourier Transform or Inverse Fast Fourier Transform (FFT/IFFT) computation satisfies Parseval's theorem, that is, the energy of the output data is N times of the energy of input data. That means, for lossless computation, it needs a large storage space, and a computation unit also needs a large bitwidth. In order to conserve resources, a conventional method comprises scanning the maximum value of the overall data, and determining a constant of powers of 2 with which the overall data is multiplied or divided to save an effective data with less bitwidth, and compensating the same factor on the final iteration result, after completion of each of iterations. This conventional method is called automatic gain. However, the above algorithm has an accuracy issue, especially when the data distribution is uneven, which causes the computation accuracy to decrease dramatically. The reason is that, when the dynamic range of data is larger than the data bitwidth, in order to represent the maximum value, all the data are scaled, which may cause the complete loss of smaller data. Therefore it is desirable to have a method and device that may solve the above problem.